SOTAVerified

Language Modelling

A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.

Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.

Source: Wikipedia

Papers

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Showing 16901–16950 of 17610 papers

TitleStatusHype
CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation—0
CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation—0
Cryptocurrency Day Trading and Framing Prediction in Microblog Discourse—0
A Generation Framework with Strict Constraints for Crystal Materials Design—0
Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials—0
C-SAW: Self-Supervised Prompt Learning for Image Generalization in Remote Sensing—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning—0
CTC-Assisted LLM-Based Contextual ASR—0
CTP-LLM: Clinical Trial Phase Transition Prediction Using Large Language Models—0
CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning—0
CTRL: Connect Collaborative and Language Model for CTR Prediction—0
CtrlDiff: Boosting Large Diffusion Language Models with Dynamic Block Prediction and Controllable Generation—0
CtrlRAG: Black-box Adversarial Attacks Based on Masked Language Models in Retrieval-Augmented Language Generation—0
CubeRobot: Grounding Language in Rubik's Cube Manipulation via Vision-Language Model—0
CUDA-Accelerated Soft Robot Neural Evolution with Large Language Model Supervision—0
Cue-bot: A Conversational Agent for Assistive Technology—0
CueBot: Cue-Controlled Response Generation for Assistive Interaction Usages—0
Cued@wmt19:ewc&lms—0
CUED@WMT19:EWC\&LMs—0
CUE-M: Contextual Understanding and Enhanced Search with Multimodal Large Language Model—0
Cue Me In: Content-Inducing Approaches to Interactive Story Generation—0
CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification—0
CUIfy the XR: An Open-Source Package to Embed LLM-powered Conversational Agents in XR—0
Cumulative Progress in Language Models for Information Retrieval—0
CUNI in WMT14: Chimera Still Awaits Bellerophon—0
CurateGPT: A flexible language-model assisted biocuration tool—0
Current Topological and Machine Learning Applications for Bias Detection in Text—0
Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding—0
Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding—0
Curriculum-Based Neighborhood Sampling For Sequence Prediction—0
Curriculum Design for Code-switching: Experiments with Language Identification and Language Modeling with Deep Neural Networks—0
Customer Sentiment Analysis using Weak Supervision for Customer-Agent Chat—0
Customising General Large Language Models for Specialised Emotion Recognition Tasks—0
Customizing a Large Language Model for VHDL Design of High-Performance Microprocessors—0
Customizing Contextualized Language Models forLegal Document Reviews—0
Customizing Language Model Responses with Contrastive In-Context Learning—0
Customizing Large Language Model Generation Style using Parameter-Efficient Finetuning—0
Customizing Speech Recognition Model with Large Language Model Feedback—0
Concealed Data Poisoning Attacks on NLP Models—0
Cut the CARP: Fishing for zero-shot story evaluation—0
Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems—0
Cut the noise: Mutually reinforcing reordering and alignments for improved machine translation—0
Cutting the Long Tail: Hybrid Language Models for Translation Style Adaptation—0
CVE-driven Attack Technique Prediction with Semantic Information Extraction and a Domain-specific Language Model—0
CVE-LLM : Automatic vulnerability evaluation in medical device industry using large language models—0
CxLM: A Construction and Context-aware Language Model—0
CXP949 at WNUT-2020 Task 2: Extracting Informative COVID-19 Tweets - RoBERTa Ensembles and The Continued Relevance of Handcrafted Features—0
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment—0
Cycle-consistency training for end-to-end speech recognition—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified